Phase 7: Speech Synthesis & Applications · ~75 minutes · Python
Voice Anti-Spoofing & Audio Watermarking — ASVspoof 5, AudioSeal, WaveVerify
Voice cloning shipped faster than defenses. 2026 production voice systems need two things: a detector (AASIST, RawNet2) that classifies real vs fake speech, and a watermark (AudioSeal) that survives compression and editing. Ship both or do not ship voice cloning.
Hiring signal: Understanding of voice anti-spoofing & audio watermarking — asvspoof 5, audioseal, waveverify internals
What you will learn
- Implement voice anti-spoofing & audio watermarking — asvspoof 5, audioseal, waveverify from scratch
- Understand the math and intuition behind the algorithm
- Use production libraries for the same task
- Ship a reusable artifact
Introduction
Type: Build Languages: Python Prerequisites: Phase 6 · 06 (Speaker Recognition), Phase 6 · 08 (Voice Cloning) Time: ~75 minutes
The Problem
Three related defenses:
- Anti-spoofing / deepfake detection. Given an audio clip, is it synthetic or real? ASVspoof benchmarks (ASVspoof 2019 → 2021 → 5) are the gold standard.
- Audio watermarking. Embed an imperceptible signal in generated audio that a detector can extract later. AudioSeal (Meta) and WavMark are the open options.
- Authenticated provenance. Cryptographic signing of audio files + metadata. C2PA / Content Authenticity Initiative.
Detection handles adversaries who don't cooperate. Watermarking handles compliance — AI-generated audio should be identifiable as such. Both are required in 2026.
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You've read the first 2 sections. The rest of this lesson covers The Concept, Build It, Use It, Pitfalls, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.
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- Audio Evaluation — WER, MOS, UTMOS, MMAU, FAD, and the Open Leaderboards
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